A theory of cerebellar cortex

By D. Marr. 1969

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Second, substantial theoretical work based on Marr-Albus theory has shown that the circuitry of the input layer of the cerebellar cortex is well suited for pattern separation (Marr, 1969; Albus, 1971; Kanerva, 1988; Tyrrell and Willshaw, 1992; Billings et al., 2014; Cayco-Gajic et al., 2017; Litwin-”
Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex (1982, background): “It is known that various algorithms related to Hebb’s principle of synaptic modification (Hebb, 1949) can account for the formation of associative and distributed memories (see, for example, Marr, 1969; Brindley, 1969; Anderson, 1970, 1972; Cooper, 1973; Kohonen, 1977).”
Toward an Integration of Deep Learning and Neuroscience (2016, background): “…has more connections than the rest of the brain taken together, apparently in a largely feedforward architecture, and the tiny cerebellar granule cells, which may form a randomized high-dimensional input representation (Marr, 1969; Jacobson and Friedrich, 2013), outnumber all other neurons.”
Memory as a Constructive Process: The Parallel Distributed Processing Approach (2010, background): “A detailed theory of how sparse representations minimize interference has been developed with contributions from a number of memory researchers, (29).”
Computational principles of sensorimotor control that minimize uncertainty and variability (2007, background): “The efﬁcacy of these synapses appears to be modulated by inputfromclimbingﬁbres,originatingintheinferiorolive, vialong-termdepression(Marr,1969;Albus,1971;Fujita, 1982;Ito,2001).”
Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity (2017, background): “Expansion recoding is a leading hypothesis for the role of the dense cerebellar granule cell layer (GCL) (Marr, 1969; Albus, 1971).”
Olfactory representations by Drosophila mushroom body neurons. (2008, background): “Sparse representations are useful in memory networks for at least two related reasons (Kanerva 1988; Marr 1969; Olshausen and Field 2004; Tsodyks 1988): if an item is represented by relatively few neurons, memorization of this item requires the modification of only a few synaptic nodes; if…”
Statistical mechanics of complex neural systems and high dimensional data (2013, background): “Indeed, an analogy between the Purkinje cell and the perceptron was first posited over 40 years ago [82, 83].”
The neuroscience of mammalian associative learning. (2005, background): “Early cerebellar learning theories proposed plasticity within the cerebellar cortex, specifically at the parallel fiber-Purkinje cell (PF-PC) synapses, as a general mechanism of motor learning (Albus 1971, Marr 1969).”
Granule Cell Ascending Axon Excitatory Synapses onto Golgi Cells Implement a Potent Feedback Circuit in the Cerebellar Granular Layer (2013, background): “GoCs may control GrC excitation by performing lateral inhibition and gain control (Eccles et al., 1967; Marr, 1969; Albus, 1971), by creating oscillatory synchronization (Maex and De Schutter, 1998; Dugué et al., 2009; Vervaeke et al., 2010), by setting the time window for GrC excitation (D’Angelo…”
A Simple Connectivity Scheme for Sparse Coding in an Olfactory System (2007, background): “Marr (1969) and Albus (1971) were among the very first to address this issue.”
Integration of quanta in cerebellar granule cells during sensory processing (2004, result): “Our findings provide the first direct evidence in support of theoretical predictions that the spontaneous firing rate of granule cells in vivo is low, which will increase the number of distinct patterns of input that can be represented by the cerebellar corte”
Neural primitives for motion control (2004, background): “V. INTERNAL MODELS AS A BASIS FOR ADAPTIVE BEHAVIOR The ability to generate a variety of complex behaviors cannot be attained by just storing the control signals for each action and recalling these signals when subsequently needed [65], [66].”
Path integration and the neural basis of the 'cognitive map' (2006, unknown): “Based on analogies with the cerebellum, where decorrelation has been postulated to be accomplished by the dispersal of incoming sensory information onto a vastly expanded layer of granule cells before the information reaches the associative synapses of the Purkinje cell”